pyspark.sql.functions.try_aes_decrypt¶

pyspark.sql.functions.try_aes_decrypt(input: ColumnOrName, key: ColumnOrName, mode: Optional[ColumnOrName] = None, padding: Optional[ColumnOrName] = None, aad: Optional[ColumnOrName] = None) → pyspark.sql.column.Column[source]¶

This is a special version of aes_decrypt that performs the same operation, but returns a NULL value instead of raising an error if the decryption cannot be performed. Returns a decrypted value of input using AES in mode with padding. Key lengths of 16, 24 and 32 bits are supported. Supported combinations of (mode, padding) are (‘ECB’, ‘PKCS’), (‘GCM’, ‘NONE’) and (‘CBC’, ‘PKCS’). Optional additional authenticated data (AAD) is only supported for GCM. If provided for encryption, the identical AAD value must be provided for decryption. The default mode is GCM.

New in version 3.5.0.

Parameters

inputColumn or str: The binary value to decrypt.
keyColumn or str: The passphrase to use to decrypt the data.
modeColumn or str, optional: Specifies which block cipher mode should be used to decrypt messages. Valid modes: ECB, GCM, CBC.
paddingColumn or str, optional: Specifies how to pad messages whose length is not a multiple of the block size. Valid values: PKCS, NONE, DEFAULT. The DEFAULT padding means PKCS for ECB, NONE for GCM and PKCS for CBC.
aadColumn or str, optional: Optional additional authenticated data. Only supported for GCM mode. This can be any free-form input and must be provided for both encryption and decryption.

Examples

>>> df = spark.createDataFrame([(
...     "AAAAAAAAAAAAAAAAQiYi+sTLm7KD9UcZ2nlRdYDe/PX4",
...     "abcdefghijklmnop12345678ABCDEFGH", "GCM", "DEFAULT",
...     "This is an AAD mixed into the input",)],
...     ["input", "key", "mode", "padding", "aad"]
... )
>>> df.select(try_aes_decrypt(
...     unbase64(df.input), df.key, df.mode, df.padding, df.aad).alias('r')
... ).collect()
[Row(r=bytearray(b'Spark'))]

>>> df = spark.createDataFrame([(
...     "AAAAAAAAAAAAAAAAAAAAAPSd4mWyMZ5mhvjiAPQJnfg=",
...     "abcdefghijklmnop12345678ABCDEFGH", "CBC", "DEFAULT",)],
...     ["input", "key", "mode", "padding"]
... )
>>> df.select(try_aes_decrypt(
...     unbase64(df.input), df.key, df.mode, df.padding).alias('r')
... ).collect()
[Row(r=bytearray(b'Spark'))]

>>> df.select(try_aes_decrypt(unbase64(df.input), df.key, df.mode).alias('r')).collect()
[Row(r=bytearray(b'Spark'))]

>>> df = spark.createDataFrame([(
...     "83F16B2AA704794132802D248E6BFD4E380078182D1544813898AC97E709B28A94",
...     "0000111122223333",)],
...     ["input", "key"]
... )
>>> df.select(try_aes_decrypt(unhex(df.input), df.key).alias('r')).collect()
[Row(r=bytearray(b'Spark'))]

pyspark.sql.functions.stack

pyspark.sql.functions.typeof